1. Field of the Invention
The present invention relates to a global spatial domain detail controlling method, and more particularly, to a global spatial domain detail controlling method capable of adjusting corresponding detail parameters for image processing according to space position of each pixel in an image, so as to process a center portion and a corner portion of the image separately, wherein the corner portion of the image may exhibit larger noise due to lack of illumination.
2. Description of the Prior Art
In general, an image sensor is designed toward less area, more pixels and smaller pixels. However, when the image sensor is used with a lens, a corner portion of the image may be obscured by the lens, which causes lens shading and leads to even less illumination such that the captured image exhibits larger noise in corner portions (i.e. lens shading usually attenuates with multiple functions, and SNR attenuates along with lens shading).
In such a condition, a conventional image processor usually does not take spatial factors into consideration when performing image processing manners such as noise reduction control, sharpness control, color saturation control and color interpolation control, and may sacrifice the image quality of the center portion in order to take the image quality of the corner portions into account.
For example, please refer to FIG. 1, which is a schematic diagram of noise reduction control performed by a conventional image processor. As shown in FIG. 1, when performing noise reduction control, the conventional image processor generates a noise reduction prediction image NRP by low-pass filtering an original image ORI1 (i.e. taking a weighted average to each pixel with surrounding pixels thereof for generating a new pixel value), and then generates a noise reduction image NR from a weighted average of the original image ORI1− and the noise reduction prediction image NRP with a specific weighting factor W (i.e. NR=(1−W)*ORI1+W*NRP). Since a single specific weighting factor W is used for all pixels in the entire image, the weighting factor of the noise reduction prediction image NRP is increased in order to reduce noise for the corner portion of the noise reduction image NR, which however may blur the center portion of the noise reduction image NR.
Besides, please refer to FIG. 2, which is a schematic diagram of sharpness control performed by the conventional image processor. As shown in FIG. 2, when performing sharpness control, the conventional image processor generates an edge extraction image EE by extracting edges in the original image ORI1 (i.e. taking a pixel value difference between each pixel and a front or rear pixel thereof in a specific direction so that the pixel value difference becomes plus or minus near an edge due to an decrease or increase of the pixel value of the front or rear pixel), and then generates an edge extraction gain image EEG by enlarging the pixel value difference in the edge extraction image EE with an edge gain. Finally, the conventional image processor sums up the original image ORI1 with the edge extraction gain image EEG to obtain a sharpened image SHA in order to strengthen pixel value differences at the edges for sharpness control. Since the pixel value differences in the entire image are enlarged with the same edge gain, the edge gain is increased in order to sufficiently sharpen the corner portion of the sharpened image SHA for noise reduction, which however, may over-sharpen the center portion of the sharpened image SHA and thereby distort the image.
Moreover, please refer to FIG. 3, which is a schematic diagram of color saturation control performed by the conventional image processor. As shown in FIG. 3, when performing color saturation control, the conventional image processor adjusts the color saturation of the original image ORI2. For example, when decreasing a color saturation gain to generate a low color saturation image LSG, the image texture is clear at the lower right of the low color saturation image LSG but the image color is worse, and when increasing a color saturation gain to generate a high color saturation image HSG, the image color of the high color saturation image HSG is better but the image texture at the lower right is not clear (because the noise is too high in the corner portion to determine the color correctly). Since the entire image is controlled by the same color saturation gain, the color saturation gain is decreased in order to make the image texture of the corner portion clear, which however, may worsen the image color of the center portion and thereby distort the image.
In addition, please refer to FIG. 4, which is a schematic diagram of color interpolation control performed by the conventional image processor. As shown in FIG. 4, the conventional image processor simplifies the image with a Bayer Pattern. Each pixel only has a primary color data, and requires obtaining the other two primary colors by interpolation. Therefore, when the conventional image processor performs color interpolation control to obtain a green pixel value G5 at a position only having a red pixel value R5 by interpolating green pixel values G2, G4, G6, G8 at the surroundings, the conventional image processor obtains a horizontal pixel value difference dH and a vertical pixel value difference dV (i.e. dV=|G2−G8| and dH=|G4−G6|), and then compares the horizontal pixel value difference dH and the vertical pixel value difference dV with a color interpolation threshold CIT. If the horizontal pixel value difference dH is larger than the color interpolation threshold CIT and the vertical pixel value difference dV is smaller than the color interpolation threshold CIT, the conventional image processor determines that the interpolation is performed in the vertical direction (i.e. G5=(G2+G8)/2), if the horizontal pixel value difference dH is smaller than the color interpolation threshold CIT and the vertical pixel value difference dV is larger than the color interpolation threshold CIT, the conventional image processor determines that the interpolation is performed in the horizontal direction (i.e. G5=(G4+G6)/2), and if the horizontal pixel value difference dH is smaller than the color interpolation threshold CIT and the vertical pixel value difference dV is smaller than the color interpolation threshold CIT, the conventional image processor determines that the interpolation is performed as no particular direction (i.e. G5=(G2+G4+G6+G8)/4). Since the entire image is controlled by the same color interpolation threshold CIT, the color interpolation threshold CIT is increased in order to prevent the corner portion from an incorrect determination of the direction due to noise (i.e. when the noise is larger, the interpolation directions are randomly determined as vertical or horizontal direction in an area so that a maze texture may be formed), which however, may not clearly determine the direction for the center portion of the image.
From the above, when performing image processing manners such as noise reduction control, sharpness control, color saturation control and color interpolation control, the conventional image processor usually does not take spatial factors into consideration and uses the same detail parameters for processing the entire image. Therefore, the image quality of the center portion may be sacrificed in order to take the image quality of the corner portions into account. Thus, there is a need for improvement of the prior art.